Robustness of coupled oscillator networks against local degradation of oscillators has been intensively studied in this decade. The oscillation behavior on the whole network is typically reduced with an increase in the fraction of degraded (inactive) oscillators. The critical fraction of inactive oscillators, at which a transition from an oscillatory to a quiescent state occurs, has been used as a measure for the network robustness. The larger (smaller) this measure is, the more robust (fragile) the oscillatory behavior on the network is. Most previous studies have used oscillators with identical natural frequencies, for which the oscillators are necessarily synchronized and thereby the analysis is simple. In contrast, we focus on the effect of heterogeneity in the natural frequencies on the network robustness. First, we analytically derive the robustness measure for the coupled oscillator models with heterogeneous natural frequencies under some conditions. Then, we show that increasing the heterogeneity in natural frequencies makes the network fragile. Moreover, we discuss the optimal parameter condition to maximize the network robustness.

1.
A. T.
Winfree
, “
Biological rhythms and the behavior of populations of coupled oscillators
,”
J. Theor. Biol.
16
,
15
42
(
1967
).
2.
S. H.
Strogatz
,
I.
Stewart
 et al., “
Coupled oscillators and biological synchronization
,”
Sci. Am.
269
,
102
109
(
1993
).
3.
A.
Pikovsky
,
M.
Rosenblum
, and
J.
Kurths
,
Synchronization: A Universal Concept in Nonlinear Sciences
(
Cambridge University Press
,
2003
), Vol.
12
.
4.
J. A.
Acebrón
,
L. L.
Bonilla
,
C. J. P.
Vicente
,
F.
Ritort
, and
R.
Spigler
, “
The Kuramoto model: A simple paradigm for synchronization phenomena
,”
Rev. Mod. Phys.
77
,
137
(
2005
).
5.
Y.
Kuramoto
,
Chemical Oscillations, Waves, and Turbulence
(
Springer Science & Business Media
,
2012
), Vol.
19
.
6.
R. E.
Mirollo
and
S. H.
Strogatz
, “
Amplitude death in an array of limit-cycle oscillators
,”
J. Stat. Phys.
60
,
245
262
(
1990
).
7.
G. B.
Ermentrout
, “
Oscillator death in populations of all to all coupled nonlinear oscillators
,”
Phys. D
41
,
219
231
(
1990
).
8.
A.
Koseska
,
E.
Volkov
, and
J.
Kurths
, “
Oscillation quenching mechanisms: Amplitude vs. oscillation death
,”
Phys. Rep.
531
,
173
199
(
2013
).
9.
H.
Daido
and
K.
Nakanishi
, “
Aging transition and universal scaling in oscillator networks
,”
Phys. Rev. Lett.
93
,
104101
(
2004
).
10.
H.
Daido
and
K.
Nakanishi
, “
Aging and clustering in globally coupled oscillators
,”
Phys. Rev. E
75
,
056206
(
2007
).
11.
G.
Tanaka
,
K.
Morino
, and
K.
Aihara
, “
Dynamical robustness in complex networks: The crucial role of low-degree nodes
,”
Sci. Rep.
2
,
232
(
2012
).
12.
K.
Morino
,
G.
Tanaka
, and
K.
Aihara
, “
Robustness of multilayer oscillator networks
,”
Phys. Rev. E
83
,
056208
(
2011
).
13.
T.
Sasai
,
K.
Morino
,
G.
Tanaka
,
J. A.
Almendral
, and
K.
Aihara
, “
Robustness of oscillatory behavior in correlated networks
,”
PLOS One
10
,
e0123722
(
2015
).
14.
D.
Pazó
and
E.
Montbrió
, “
Universal behavior in populations composed of excitable and self-oscillatory elements
,”
Phys. Rev. E
73
,
055202
(
2006
).
15.
G.
Tanaka
,
Y.
Okada
, and
K.
Aihara
, “
Phase transitions in mixed populations composed of two types of self-oscillatory elements with different periods
,”
Phys. Rev. E
82
,
035202
(
2010
).
16.
H.
Daido
and
K.
Nishio
, “
Bifurcation and scaling at the aging transition boundary in globally coupled excitable and oscillatory units
,”
Phys. Rev. E
93
,
052226
(
2016
).
17.
H.
Daido
,
A.
Kasama
, and
K.
Nishio
, “
Onset of dynamic activity in globally coupled excitable and oscillatory units
,”
Phys. Rev. E
88
,
052907
(
2013
).
18.
W.
Huang
,
X.
Zhang
,
X.
Hu
,
Y.
Zou
,
Z.
Liu
, and
S.
Guan
, “
Variation of critical point of aging transition in a networked oscillators system
,”
Chaos
24
,
023122
(
2014
).
19.
T.
Yuan
,
K.
Aihara
, and
G.
Tanaka
, “
Robustness and fragility in coupled oscillator networks under targeted attacks
,”
Phys. Rev. E
95
,
012315
(
2017
).
20.
Z.
He
,
S.
Liu
, and
M.
Zhan
, “
Dynamical robustness analysis of weighted complex networks
,”
Phys. A: Stat. Mech. Appl.
392
,
4181
4191
(
2013
).
21.
H.
Daido
, “
Strong-coupling limit in heterogeneous populations of coupled oscillators
,”
Phys. Rev. E
84
,
016215
(
2011
).
22.
G.
Tanaka
,
K.
Morino
,
H.
Daido
, and
K.
Aihara
, “
Dynamical robustness of coupled heterogeneous oscillators
,”
Phys. Rev. E
89
,
052906
(
2014
).
23.
B.
Thakur
,
D.
Sharma
, and
A.
Sen
, “
Time-delay effects on the aging transition in a population of coupled oscillators
,”
Phys. Rev. E
90
,
042904
(
2014
).
24.
Z.
Sun
,
N.
Ma
, and
W.
Xu
, “
Aging transition by random errors
,”
Sci. Rep.
7
,
42715
(
2017
).
25.
G.
Tanaka
,
K.
Morino
, and
K.
Aihara
, “
Dynamical robustness of complex biological networks
,” in
Mathematical Approaches to Biological Systems
(
Springer
,
2015
), pp.
29
53
.
26.
K.
Morino
,
G.
Tanaka
, and
K.
Aihara
, “
Efficient recovery of dynamic behavior in coupled oscillator networks
,”
Phys. Rev. E
88
,
032909
(
2013
).
27.
Y.
Liu
,
W.
Zou
,
M.
Zhan
,
J.
Duan
, and
J.
Kurths
, “
Enhancing dynamical robustness in aging networks of coupled nonlinear oscillators
,”
EPL (Europhys. Lett.)
114
,
40004
(
2016
).
28.
R.
Pastor-Satorras
and
A.
Vespignani
, “
Epidemic spreading in scale-free networks
,”
Phys. Rev. Lett.
86
,
3200
(
2001
).
29.
C. R.
Laing
,
Y.
Zou
,
B.
Smith
, and
I. G.
Kevrekidis
, “
Managing heterogeneity in the study of neural oscillator dynamics
,”
J. Math. Neurosci.
2
,
5
(
2012
).
30.
F.
Dörfler
,
M.
Chertkov
, and
F.
Bullo
, “
Synchronization in complex oscillator networks and smart grids
,”
Proc. Natl. Acad. Sci.
110
,
2005
2010
(
2013
).
You do not currently have access to this content.